Overview

Dataset statistics

Number of variables44
Number of observations20885
Missing cells80569
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.7 MiB
Average record size in memory1.1 KiB

Variable types

Categorical15
Numeric29

Alerts

DOB has a high cardinality: 14007 distinct values High cardinality
DOD has a high cardinality: 4901 distinct values High cardinality
ADMITTIME has a high cardinality: 19714 distinct values High cardinality
DISCHTIME has a high cardinality: 19706 distinct values High cardinality
DEATHTIME has a high cardinality: 2093 distinct values High cardinality
DIAGNOSIS has a high cardinality: 6193 distinct values High cardinality
ICD9_diagnosis has a high cardinality: 1853 distinct values High cardinality
HeartRate_Min is highly correlated with HeartRate_MeanHigh correlation
HeartRate_Max is highly correlated with HeartRate_MeanHigh correlation
HeartRate_Mean is highly correlated with HeartRate_Min and 1 other fieldsHigh correlation
SysBP_Min is highly correlated with SysBP_Mean and 4 other fieldsHigh correlation
SysBP_Max is highly correlated with SysBP_Mean and 3 other fieldsHigh correlation
SysBP_Mean is highly correlated with SysBP_Min and 5 other fieldsHigh correlation
DiasBP_Min is highly correlated with SysBP_Min and 3 other fieldsHigh correlation
DiasBP_Max is highly correlated with SysBP_Max and 3 other fieldsHigh correlation
DiasBP_Mean is highly correlated with SysBP_Min and 6 other fieldsHigh correlation
MeanBP_Min is highly correlated with SysBP_Min and 4 other fieldsHigh correlation
MeanBP_Max is highly correlated with SysBP_Max and 4 other fieldsHigh correlation
MeanBP_Mean is highly correlated with SysBP_Min and 7 other fieldsHigh correlation
RespRate_Min is highly correlated with RespRate_MeanHigh correlation
RespRate_Max is highly correlated with RespRate_MeanHigh correlation
RespRate_Mean is highly correlated with RespRate_Min and 1 other fieldsHigh correlation
TempC_Min is highly correlated with TempC_MeanHigh correlation
TempC_Max is highly correlated with TempC_MeanHigh correlation
TempC_Mean is highly correlated with TempC_Min and 1 other fieldsHigh correlation
SpO2_Min is highly correlated with SpO2_MeanHigh correlation
SpO2_Max is highly correlated with SpO2_MeanHigh correlation
SpO2_Mean is highly correlated with SpO2_Min and 1 other fieldsHigh correlation
Glucose_Min is highly correlated with Glucose_MeanHigh correlation
Glucose_Max is highly correlated with Glucose_MeanHigh correlation
Glucose_Mean is highly correlated with Glucose_Min and 1 other fieldsHigh correlation
RELIGION is highly correlated with ETHNICITYHigh correlation
ETHNICITY is highly correlated with RELIGIONHigh correlation
HeartRate_Min has 2187 (10.5%) missing values Missing
HeartRate_Max has 2187 (10.5%) missing values Missing
HeartRate_Mean has 2187 (10.5%) missing values Missing
SysBP_Min has 2208 (10.6%) missing values Missing
SysBP_Max has 2208 (10.6%) missing values Missing
SysBP_Mean has 2208 (10.6%) missing values Missing
DiasBP_Min has 2209 (10.6%) missing values Missing
DiasBP_Max has 2209 (10.6%) missing values Missing
DiasBP_Mean has 2209 (10.6%) missing values Missing
MeanBP_Min has 2186 (10.5%) missing values Missing
MeanBP_Max has 2186 (10.5%) missing values Missing
MeanBP_Mean has 2186 (10.5%) missing values Missing
RespRate_Min has 2189 (10.5%) missing values Missing
RespRate_Max has 2189 (10.5%) missing values Missing
RespRate_Mean has 2189 (10.5%) missing values Missing
TempC_Min has 2497 (12.0%) missing values Missing
TempC_Max has 2497 (12.0%) missing values Missing
TempC_Mean has 2497 (12.0%) missing values Missing
SpO2_Min has 2203 (10.5%) missing values Missing
SpO2_Max has 2203 (10.5%) missing values Missing
SpO2_Mean has 2203 (10.5%) missing values Missing
Glucose_Min has 253 (1.2%) missing values Missing
Glucose_Max has 253 (1.2%) missing values Missing
Glucose_Mean has 253 (1.2%) missing values Missing
DOD has 13511 (64.7%) missing values Missing
DEATHTIME has 18540 (88.8%) missing values Missing
MARITAL_STATUS has 722 (3.5%) missing values Missing
DOB is uniformly distributed Uniform
DOD is uniformly distributed Uniform
ADMITTIME is uniformly distributed Uniform
DISCHTIME is uniformly distributed Uniform
DEATHTIME is uniformly distributed Uniform
icustay_id has unique values Unique

Reproduction

Analysis started2022-09-14 15:14:52.313407
Analysis finished2022-09-14 15:17:48.445249
Duration2 minutes and 56.13 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
18540 
1
2345 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20885
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018540
88.8%
12345
 
11.2%

Length

2022-09-14T23:17:48.602598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-14T23:17:48.846712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
018540
88.8%
12345
 
11.2%

Most occurring characters

ValueCountFrequency (%)
018540
88.8%
12345
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20885
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018540
88.8%
12345
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common20885
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018540
88.8%
12345
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII20885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018540
88.8%
12345
 
11.2%

subject_id
Real number (ℝ≥0)

Distinct16317
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58950.4961
Minimum23
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:49.035875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile14483.8
Q141132
median60441
Q380286
95-th percentile96137
Maximum99999
Range99976
Interquartile range (IQR)39154

Descriptive statistics

Standard deviation25299.43954
Coefficient of variation (CV)0.4291641497
Kurtosis-0.8660307541
Mean58950.4961
Median Absolute Deviation (MAD)19566
Skewness-0.2740365718
Sum1231181111
Variance640061640.8
MonotonicityNot monotonic
2022-09-14T23:17:49.259791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10925
 
0.1%
2365716
 
0.1%
7371316
 
0.1%
506015
 
0.1%
2903514
 
0.1%
1186113
 
0.1%
7632712
 
0.1%
3126012
 
0.1%
780911
 
0.1%
395211
 
0.1%
Other values (16307)20740
99.3%
ValueCountFrequency (%)
231
 
< 0.1%
341
 
< 0.1%
361
 
< 0.1%
851
 
< 0.1%
10925
0.1%
1111
 
< 0.1%
1242
 
< 0.1%
1651
 
< 0.1%
1884
 
< 0.1%
1911
 
< 0.1%
ValueCountFrequency (%)
999991
< 0.1%
999951
< 0.1%
999851
< 0.1%
999831
< 0.1%
999822
< 0.1%
999661
< 0.1%
999651
< 0.1%
999571
< 0.1%
999551
< 0.1%
999461
< 0.1%

hadm_id
Real number (ℝ≥0)

Distinct19749
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150082.4023
Minimum100001
Maximum199999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:49.428123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile104798.2
Q1125157
median150152
Q3175017
95-th percentile194861
Maximum199999
Range99998
Interquartile range (IQR)49860

Descriptive statistics

Standard deviation28898.47985
Coefficient of variation (CV)0.1925507548
Kurtosis-1.202874565
Mean150082.4023
Median Absolute Deviation (MAD)24940
Skewness-0.01030003285
Sum3134470972
Variance835122137.4
MonotonicityNot monotonic
2022-09-14T23:17:49.591906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1647685
 
< 0.1%
1754485
 
< 0.1%
1311185
 
< 0.1%
1855184
 
< 0.1%
1019744
 
< 0.1%
1277134
 
< 0.1%
1231784
 
< 0.1%
1296114
 
< 0.1%
1378694
 
< 0.1%
1807144
 
< 0.1%
Other values (19739)20842
99.8%
ValueCountFrequency (%)
1000011
< 0.1%
1000031
< 0.1%
1000091
< 0.1%
1000101
< 0.1%
1000111
< 0.1%
1000121
< 0.1%
1000161
< 0.1%
1000181
< 0.1%
1000201
< 0.1%
1000241
< 0.1%
ValueCountFrequency (%)
1999991
< 0.1%
1999981
< 0.1%
1999921
< 0.1%
1999841
< 0.1%
1999791
< 0.1%
1999721
< 0.1%
1999581
< 0.1%
1999571
< 0.1%
1999561
< 0.1%
1999491
< 0.1%

icustay_id
Real number (ℝ≥0)

UNIQUE

Distinct20885
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250202.4955
Minimum200001
Maximum299998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:49.787809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum200001
5-th percentile204874
Q1225153
median250452
Q3275303
95-th percentile295111.8
Maximum299998
Range99997
Interquartile range (IQR)50150

Descriptive statistics

Standard deviation28909.8063
Coefficient of variation (CV)0.1155456353
Kurtosis-1.201637302
Mean250202.4955
Median Absolute Deviation (MAD)25081
Skewness-0.01591050828
Sum5225479119
Variance835776900.4
MonotonicityNot monotonic
2022-09-14T23:17:49.968231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2283571
 
< 0.1%
2529491
 
< 0.1%
2861281
 
< 0.1%
2968491
 
< 0.1%
2625261
 
< 0.1%
2443901
 
< 0.1%
2547571
 
< 0.1%
2747521
 
< 0.1%
2433631
 
< 0.1%
2803271
 
< 0.1%
Other values (20875)20875
> 99.9%
ValueCountFrequency (%)
2000011
< 0.1%
2000101
< 0.1%
2000161
< 0.1%
2000211
< 0.1%
2000241
< 0.1%
2000281
< 0.1%
2000331
< 0.1%
2000341
< 0.1%
2000351
< 0.1%
2000381
< 0.1%
ValueCountFrequency (%)
2999981
< 0.1%
2999951
< 0.1%
2999861
< 0.1%
2999721
< 0.1%
2999621
< 0.1%
2999571
< 0.1%
2999561
< 0.1%
2999501
< 0.1%
2999481
< 0.1%
2999471
< 0.1%

HeartRate_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct131
Distinct (%)0.7%
Missing2187
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean69.70590437
Minimum2
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:50.164136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile47
Q160
median69
Q379
95-th percentile95
Maximum141
Range139
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.8698401
Coefficient of variation (CV)0.2133225332
Kurtosis0.6914497587
Mean69.70590437
Median Absolute Deviation (MAD)9
Skewness0.2005730859
Sum1303361
Variance221.1121446
MonotonicityNot monotonic
2022-09-14T23:17:50.368636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60682
 
3.3%
70624
 
3.0%
69555
 
2.7%
67529
 
2.5%
63529
 
2.5%
64520
 
2.5%
68515
 
2.5%
62499
 
2.4%
66495
 
2.4%
65486
 
2.3%
Other values (121)13264
63.5%
(Missing)2187
 
10.5%
ValueCountFrequency (%)
23
< 0.1%
32
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
72
< 0.1%
81
 
< 0.1%
103
< 0.1%
113
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
1411
 
< 0.1%
1371
 
< 0.1%
1351
 
< 0.1%
1333
< 0.1%
1311
 
< 0.1%
1291
 
< 0.1%
1281
 
< 0.1%
1271
 
< 0.1%
1263
< 0.1%
1251
 
< 0.1%

HeartRate_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct164
Distinct (%)0.9%
Missing2187
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean105.239801
Minimum39
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:50.537626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile75
Q190
median103
Q3118
95-th percentile143
Maximum280
Range241
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.92261332
Coefficient of variation (CV)0.1988089402
Kurtosis1.062693412
Mean105.239801
Median Absolute Deviation (MAD)14
Skewness0.6626789532
Sum1967773.8
Variance437.755748
MonotonicityNot monotonic
2022-09-14T23:17:50.700136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100413
 
2.0%
88399
 
1.9%
103389
 
1.9%
90380
 
1.8%
94379
 
1.8%
105379
 
1.8%
96378
 
1.8%
102375
 
1.8%
97375
 
1.8%
98369
 
1.8%
Other values (154)14862
71.2%
(Missing)2187
 
10.5%
ValueCountFrequency (%)
391
 
< 0.1%
462
 
< 0.1%
491
 
< 0.1%
502
 
< 0.1%
516
< 0.1%
522
 
< 0.1%
532
 
< 0.1%
541
 
< 0.1%
556
< 0.1%
566
< 0.1%
ValueCountFrequency (%)
2801
< 0.1%
2232
< 0.1%
2221
< 0.1%
2191
< 0.1%
2161
< 0.1%
2151
< 0.1%
2131
< 0.1%
2121
< 0.1%
2071
< 0.1%
2061
< 0.1%

HeartRate_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14091
Distinct (%)75.4%
Missing2187
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean85.18024963
Minimum34.71428571
Maximum163.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:50.892856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum34.71428571
5-th percentile61.75
Q174.27272727
median84.1311828
Q395.18558752
95-th percentile112.0840686
Maximum163.875
Range129.1607143
Interquartile range (IQR)20.91286024

Descriptive statistics

Standard deviation15.31820849
Coefficient of variation (CV)0.1798328668
Kurtosis0.07125286594
Mean85.18024963
Median Absolute Deviation (MAD)10.41007168
Skewness0.332195826
Sum1592700.307
Variance234.6475113
MonotonicityNot monotonic
2022-09-14T23:17:51.092150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7324
 
0.1%
7823
 
0.1%
8822
 
0.1%
8021
 
0.1%
7621
 
0.1%
8320
 
0.1%
7519
 
0.1%
7918
 
0.1%
7417
 
0.1%
9216
 
0.1%
Other values (14081)18497
88.6%
(Missing)2187
 
10.5%
ValueCountFrequency (%)
34.714285711
< 0.1%
35.093751
< 0.1%
35.739130431
< 0.1%
37.958333331
< 0.1%
38.318181821
< 0.1%
38.51
< 0.1%
38.833333331
< 0.1%
39.866666671
< 0.1%
39.93751
< 0.1%
40.269230771
< 0.1%
ValueCountFrequency (%)
163.8751
< 0.1%
149.67647061
< 0.1%
149.11
< 0.1%
1491
< 0.1%
147.02247191
< 0.1%
146.46913581
< 0.1%
146.07407411
< 0.1%
145.66666671
< 0.1%
143.69444441
< 0.1%
141.968751
< 0.1%

SysBP_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct154
Distinct (%)0.8%
Missing2208
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean91.1105638
Minimum5
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:51.280940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile64
Q181
median90
Q3101
95-th percentile121
Maximum181
Range176
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.53253361
Coefficient of variation (CV)0.1924314029
Kurtosis1.05658007
Mean91.1105638
Median Absolute Deviation (MAD)10
Skewness0.1174282132
Sum1701672
Variance307.3897347
MonotonicityNot monotonic
2022-09-14T23:17:51.465826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90566
 
2.7%
91536
 
2.6%
85515
 
2.5%
82506
 
2.4%
87494
 
2.4%
86479
 
2.3%
94479
 
2.3%
88474
 
2.3%
83465
 
2.2%
89464
 
2.2%
Other values (144)13699
65.6%
(Missing)2208
 
10.6%
ValueCountFrequency (%)
51
< 0.1%
71
< 0.1%
111
< 0.1%
121
< 0.1%
151
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
202
< 0.1%
222
< 0.1%
ValueCountFrequency (%)
1811
 
< 0.1%
1721
 
< 0.1%
1681
 
< 0.1%
1661
 
< 0.1%
1631
 
< 0.1%
1621
 
< 0.1%
1613
< 0.1%
1602
< 0.1%
1584
< 0.1%
1572
< 0.1%

SysBP_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct190
Distinct (%)1.0%
Missing2208
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean150.7259196
Minimum46
Maximum323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:51.822748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile117
Q1134
median148
Q3164
95-th percentile193
Maximum323
Range277
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.83379299
Coefficient of variation (CV)0.1581267048
Kurtosis1.463146646
Mean150.7259196
Median Absolute Deviation (MAD)15
Skewness0.728848589
Sum2815108
Variance568.0496885
MonotonicityNot monotonic
2022-09-14T23:17:51.980984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144375
 
1.8%
141373
 
1.8%
145357
 
1.7%
149354
 
1.7%
148343
 
1.6%
147342
 
1.6%
137341
 
1.6%
135337
 
1.6%
143337
 
1.6%
139336
 
1.6%
Other values (180)15182
72.7%
(Missing)2208
 
10.6%
ValueCountFrequency (%)
461
< 0.1%
691
< 0.1%
702
< 0.1%
751
< 0.1%
771
< 0.1%
791
< 0.1%
822
< 0.1%
832
< 0.1%
841
< 0.1%
881
< 0.1%
ValueCountFrequency (%)
3231
< 0.1%
3111
< 0.1%
2991
< 0.1%
2901
< 0.1%
2862
< 0.1%
2841
< 0.1%
2831
< 0.1%
2812
< 0.1%
2791
< 0.1%
2731
< 0.1%

SysBP_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13779
Distinct (%)73.8%
Missing2208
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean119.1454225
Minimum46
Maximum202.1724138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:52.151418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile95.97171717
Q1107.1
median116.9019608
Q3129.4651163
95-th percentile149.5978079
Maximum202.1724138
Range156.1724138
Interquartile range (IQR)22.36511628

Descriptive statistics

Standard deviation16.70150338
Coefficient of variation (CV)0.1401774657
Kurtosis0.4369570077
Mean119.1454225
Median Absolute Deviation (MAD)10.82531194
Skewness0.5616204193
Sum2225279.057
Variance278.9402151
MonotonicityNot monotonic
2022-09-14T23:17:52.346562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11326
 
0.1%
10824
 
0.1%
11622
 
0.1%
11521
 
0.1%
10620
 
0.1%
11119
 
0.1%
11018
 
0.1%
11718
 
0.1%
10718
 
0.1%
12118
 
0.1%
Other values (13769)18473
88.5%
(Missing)2208
 
10.6%
ValueCountFrequency (%)
461
< 0.1%
50.863636361
< 0.1%
57.751
< 0.1%
58.833333331
< 0.1%
59.81
< 0.1%
59.953488371
< 0.1%
601
< 0.1%
62.251
< 0.1%
62.52
< 0.1%
641
< 0.1%
ValueCountFrequency (%)
202.17241381
< 0.1%
201.08823531
< 0.1%
197.21
< 0.1%
194.90909091
< 0.1%
188.88461541
< 0.1%
188.51
< 0.1%
188.28571431
< 0.1%
187.61702131
< 0.1%
187.60714291
< 0.1%
182.96153851
< 0.1%

DiasBP_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct95
Distinct (%)0.5%
Missing2209
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean44.32785393
Minimum4
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:52.511488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile25
Q137
median44
Q351
95-th percentile65
Maximum105
Range101
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.90985227
Coefficient of variation (CV)0.2686764914
Kurtosis0.6774540228
Mean44.32785393
Median Absolute Deviation (MAD)7
Skewness0.1946089464
Sum827867
Variance141.844581
MonotonicityNot monotonic
2022-09-14T23:17:52.679575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45740
 
3.5%
42718
 
3.4%
43712
 
3.4%
44703
 
3.4%
46694
 
3.3%
41682
 
3.3%
47677
 
3.2%
48657
 
3.1%
40652
 
3.1%
39637
 
3.1%
Other values (85)11804
56.5%
(Missing)2209
 
10.6%
ValueCountFrequency (%)
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
1010
 
< 0.1%
1145
0.2%
1234
0.2%
1338
0.2%
ValueCountFrequency (%)
1051
 
< 0.1%
1031
 
< 0.1%
981
 
< 0.1%
971
 
< 0.1%
961
 
< 0.1%
941
 
< 0.1%
922
< 0.1%
913
< 0.1%
904
< 0.1%
893
< 0.1%

DiasBP_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct168
Distinct (%)0.9%
Missing2209
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean88.36078389
Minimum27
Maximum294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:52.833155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile63
Q175
median86
Q398
95-th percentile123
Maximum294
Range267
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.28555381
Coefficient of variation (CV)0.2182591978
Kurtosis4.296231537
Mean88.36078389
Median Absolute Deviation (MAD)11
Skewness1.256792701
Sum1650226
Variance371.9325857
MonotonicityNot monotonic
2022-09-14T23:17:53.010008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80477
 
2.3%
83461
 
2.2%
81456
 
2.2%
78453
 
2.2%
82446
 
2.1%
75445
 
2.1%
77440
 
2.1%
84437
 
2.1%
76429
 
2.1%
87426
 
2.0%
Other values (158)14206
68.0%
(Missing)2209
 
10.6%
ValueCountFrequency (%)
271
 
< 0.1%
291
 
< 0.1%
311
 
< 0.1%
341
 
< 0.1%
352
< 0.1%
361
 
< 0.1%
381
 
< 0.1%
401
 
< 0.1%
414
< 0.1%
421
 
< 0.1%
ValueCountFrequency (%)
2941
 
< 0.1%
2751
 
< 0.1%
2611
 
< 0.1%
2311
 
< 0.1%
2302
< 0.1%
2281
 
< 0.1%
2211
 
< 0.1%
2181
 
< 0.1%
2033
< 0.1%
1981
 
< 0.1%

DiasBP_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12694
Distinct (%)68.0%
Missing2209
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean62.51161418
Minimum17
Maximum121.4893617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:53.241366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile46.5328125
Q154.95095238
median61.42365967
Q368.95454545
95-th percentile82.4572884
Maximum121.4893617
Range104.4893617
Interquartile range (IQR)14.00359307

Descriptive statistics

Standard deviation11.01078999
Coefficient of variation (CV)0.1761399084
Kurtosis0.7698198852
Mean62.51161418
Median Absolute Deviation (MAD)6.930230269
Skewness0.6028215741
Sum1167466.906
Variance121.2374963
MonotonicityNot monotonic
2022-09-14T23:17:53.411099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6435
 
0.2%
5631
 
0.1%
6630
 
0.1%
6129
 
0.1%
6029
 
0.1%
6526
 
0.1%
5526
 
0.1%
5325
 
0.1%
6724
 
0.1%
7123
 
0.1%
Other values (12684)18398
88.1%
(Missing)2209
 
10.6%
ValueCountFrequency (%)
171
< 0.1%
24.6251
< 0.1%
24.736842111
< 0.1%
26.391304351
< 0.1%
29.210526321
< 0.1%
30.159420291
< 0.1%
30.382978721
< 0.1%
30.386363641
< 0.1%
30.41
< 0.1%
30.52
< 0.1%
ValueCountFrequency (%)
121.48936171
< 0.1%
115.07142861
< 0.1%
113.406251
< 0.1%
1121
< 0.1%
111.70731711
< 0.1%
111.65217391
< 0.1%
110.3751
< 0.1%
110.31578951
< 0.1%
108.94117651
< 0.1%
108.63934431
< 0.1%

MeanBP_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct111
Distinct (%)0.6%
Missing2186
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean56.2930638
Minimum1
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:53.578901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q149
median57
Q364
95-th percentile78
Maximum122
Range121
Interquartile range (IQR)15

Descriptive statistics

Standard deviation14.34877957
Coefficient of variation (CV)0.254894273
Kurtosis2.078520109
Mean56.2930638
Median Absolute Deviation (MAD)8
Skewness-0.6032591448
Sum1052624
Variance205.8874752
MonotonicityNot monotonic
2022-09-14T23:17:53.744515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56696
 
3.3%
58690
 
3.3%
55688
 
3.3%
57688
 
3.3%
54665
 
3.2%
60650
 
3.1%
53632
 
3.0%
61632
 
3.0%
59628
 
3.0%
51601
 
2.9%
Other values (101)12129
58.1%
(Missing)2186
 
10.5%
ValueCountFrequency (%)
146
0.2%
241
0.2%
324
0.1%
421
0.1%
527
0.1%
621
0.1%
716
 
0.1%
820
0.1%
920
0.1%
1018
 
0.1%
ValueCountFrequency (%)
1221
 
< 0.1%
1121
 
< 0.1%
1111
 
< 0.1%
1101
 
< 0.1%
1092
 
< 0.1%
1081
 
< 0.1%
1052
 
< 0.1%
1041
 
< 0.1%
1036
< 0.1%
1023
< 0.1%

MeanBP_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct247
Distinct (%)1.3%
Missing2186
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean107.074282
Minimum29
Maximum299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:53.902645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile78
Q191
median102
Q3116
95-th percentile146
Maximum299
Range270
Interquartile range (IQR)25

Descriptive statistics

Standard deviation27.58598981
Coefficient of variation (CV)0.2576341329
Kurtosis14.8973779
Mean107.074282
Median Absolute Deviation (MAD)12
Skewness3.09107009
Sum2002182
Variance760.986834
MonotonicityNot monotonic
2022-09-14T23:17:54.066414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97489
 
2.3%
99446
 
2.1%
96444
 
2.1%
100440
 
2.1%
94438
 
2.1%
93436
 
2.1%
104434
 
2.1%
92432
 
2.1%
98430
 
2.1%
103425
 
2.0%
Other values (237)14285
68.4%
(Missing)2186
 
10.5%
ValueCountFrequency (%)
291
 
< 0.1%
421
 
< 0.1%
441
 
< 0.1%
521
 
< 0.1%
543
< 0.1%
561
 
< 0.1%
581
 
< 0.1%
593
< 0.1%
604
< 0.1%
613
< 0.1%
ValueCountFrequency (%)
2992
 
< 0.1%
2987
< 0.1%
2976
< 0.1%
2964
< 0.1%
2955
< 0.1%
2946
< 0.1%
2938
< 0.1%
2923
 
< 0.1%
2913
 
< 0.1%
2902
 
< 0.1%

MeanBP_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct13116
Distinct (%)70.1%
Missing2186
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean77.60953513
Minimum25.75990676
Maximum136.6521739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:54.240828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.75990676
5-th percentile61.71776744
Q170.03390805
median76.29411765
Q384.04177433
95-th percentile97.75192308
Maximum136.6521739
Range110.8922672
Interquartile range (IQR)14.00786629

Descriptive statistics

Standard deviation11.11329946
Coefficient of variation (CV)0.1431950268
Kurtosis0.8272549465
Mean77.60953513
Median Absolute Deviation (MAD)6.894117647
Skewness0.606690526
Sum1451220.697
Variance123.5054249
MonotonicityNot monotonic
2022-09-14T23:17:54.481806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7533
 
0.2%
7931
 
0.1%
7631
 
0.1%
6528
 
0.1%
7225
 
0.1%
8323
 
0.1%
8123
 
0.1%
7423
 
0.1%
6823
 
0.1%
7822
 
0.1%
Other values (13106)18437
88.3%
(Missing)2186
 
10.5%
ValueCountFrequency (%)
25.759906761
< 0.1%
291
< 0.1%
37.51
< 0.1%
37.81
< 0.1%
37.923076921
< 0.1%
38.252
< 0.1%
40.157894741
< 0.1%
42.303921571
< 0.1%
43.372093021
< 0.1%
44.222222221
< 0.1%
ValueCountFrequency (%)
136.65217391
< 0.1%
1331
< 0.1%
132.05882351
< 0.1%
131.81
< 0.1%
130.62068971
< 0.1%
129.31251
< 0.1%
129.21428571
< 0.1%
127.36585371
< 0.1%
126.12903231
< 0.1%
126.11475411
< 0.1%

RespRate_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct32
Distinct (%)0.2%
Missing2189
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean11.91920732
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:54.666797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median12
Q314
95-th percentile18
Maximum31
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.572189186
Coefficient of variation (CV)0.299700231
Kurtosis0.7990830313
Mean11.91920732
Median Absolute Deviation (MAD)2
Skewness0.2572323069
Sum222841.5
Variance12.76053558
MonotonicityNot monotonic
2022-09-14T23:17:54.838113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
122434
11.7%
112083
10.0%
102023
9.7%
141986
9.5%
131900
9.1%
91507
7.2%
151193
5.7%
81131
5.4%
16939
 
4.5%
7723
 
3.5%
Other values (22)2777
13.3%
(Missing)2189
10.5%
ValueCountFrequency (%)
139
 
0.2%
257
 
0.3%
386
 
0.4%
4162
 
0.8%
5265
 
1.3%
6444
 
2.1%
7723
3.5%
81131
5.4%
91507
7.2%
9.51
 
< 0.1%
ValueCountFrequency (%)
311
 
< 0.1%
301
 
< 0.1%
291
 
< 0.1%
284
 
< 0.1%
277
 
< 0.1%
2616
 
0.1%
2514
 
0.1%
2429
0.1%
2333
0.2%
2263
0.3%

RespRate_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct58
Distinct (%)0.3%
Missing2189
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean27.78022037
Minimum12
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:54.989395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile20
Q123
median27
Q331
95-th percentile39
Maximum69
Range57
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.278513671
Coefficient of variation (CV)0.2260066186
Kurtosis3.098831668
Mean27.78022037
Median Absolute Deviation (MAD)4
Skewness1.244570336
Sum519379
Variance39.41973391
MonotonicityNot monotonic
2022-09-14T23:17:55.340027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
241481
 
7.1%
251425
 
6.8%
261394
 
6.7%
271336
 
6.4%
281266
 
6.1%
231192
 
5.7%
221186
 
5.7%
291090
 
5.2%
301008
 
4.8%
21813
 
3.9%
Other values (48)6505
31.1%
(Missing)2189
 
10.5%
ValueCountFrequency (%)
125
 
< 0.1%
132
 
< 0.1%
1413
 
0.1%
1514
 
0.1%
1656
 
0.3%
17127
 
0.6%
18242
 
1.2%
19370
1.8%
20658
3.2%
21813
3.9%
ValueCountFrequency (%)
692
 
< 0.1%
682
 
< 0.1%
674
< 0.1%
661
 
< 0.1%
654
< 0.1%
644
< 0.1%
632
 
< 0.1%
621
 
< 0.1%
614
< 0.1%
606
< 0.1%

RespRate_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8875
Distinct (%)47.5%
Missing2189
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean18.98585472
Minimum9.291666667
Maximum41.23529412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:55.551910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9.291666667
5-th percentile13.83333333
Q116.25
median18.38235294
Q321.10554311
95-th percentile26.22099673
Maximum41.23529412
Range31.94362745
Interquartile range (IQR)4.855543113

Descriptive statistics

Standard deviation3.849713293
Coefficient of variation (CV)0.2027674471
Kurtosis1.387915009
Mean18.98585472
Median Absolute Deviation (MAD)2.367647059
Skewness0.9285136961
Sum354959.5399
Variance14.82029244
MonotonicityNot monotonic
2022-09-14T23:17:55.708032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1894
 
0.5%
1776
 
0.4%
1674
 
0.4%
1968
 
0.3%
1553
 
0.3%
2050
 
0.2%
2146
 
0.2%
2242
 
0.2%
17.540
 
0.2%
16.534
 
0.2%
Other values (8865)18119
86.8%
(Missing)2189
 
10.5%
ValueCountFrequency (%)
9.2916666671
< 0.1%
9.3714285711
< 0.1%
9.8709677421
< 0.1%
9.8751
< 0.1%
9.9347826091
< 0.1%
9.9393939391
< 0.1%
102
< 0.1%
10.032258061
< 0.1%
10.166666671
< 0.1%
10.291666671
< 0.1%
ValueCountFrequency (%)
41.235294121
< 0.1%
40.851063831
< 0.1%
40.51
< 0.1%
40.145161291
< 0.1%
40.096774191
< 0.1%
39.310344831
< 0.1%
38.921
< 0.1%
38.333333331
< 0.1%
38.218751
< 0.1%
37.636363641
< 0.1%

TempC_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct197
Distinct (%)1.1%
Missing2497
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean36.05120761
Minimum15
Maximum40.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:55.885154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35.05555556
Q135.66666667
median36.11111111
Q336.5
95-th percentile37.05555556
Maximum40.1
Range25.1
Interquartile range (IQR)0.8333333333

Descriptive statistics

Standard deviation0.7321235004
Coefficient of variation (CV)0.02030787729
Kurtosis52.96573076
Mean36.05120761
Median Absolute Deviation (MAD)0.4444444444
Skewness-3.030847831
Sum662909.6056
Variance0.5360048199
MonotonicityNot monotonic
2022-09-14T23:17:56.060431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.111111111027
 
4.9%
35.55555556918
 
4.4%
36.44444444671
 
3.2%
36.55555556661
 
3.2%
35.83333333626
 
3.0%
36.38888889622
 
3.0%
36.33333333593
 
2.8%
36580
 
2.8%
36.66666667572
 
2.7%
36.16666667560
 
2.7%
Other values (187)11558
55.3%
(Missing)2497
 
12.0%
ValueCountFrequency (%)
151
 
< 0.1%
23.31
 
< 0.1%
26.61
 
< 0.1%
26.666666673
< 0.1%
28.41
 
< 0.1%
291
 
< 0.1%
29.91
 
< 0.1%
30.277777781
 
< 0.1%
30.51
 
< 0.1%
30.555555561
 
< 0.1%
ValueCountFrequency (%)
40.11
< 0.1%
401
< 0.1%
39.61
< 0.1%
39.222222221
< 0.1%
39.21
< 0.1%
39.166666671
< 0.1%
391
< 0.1%
38.944444441
< 0.1%
38.888888891
< 0.1%
38.833333331
< 0.1%

TempC_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct203
Distinct (%)1.1%
Missing2497
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean37.42878145
Minimum30.8
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:56.229761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30.8
5-th percentile36.33333333
Q136.94444444
median37.33333333
Q337.88888889
95-th percentile38.88888889
Maximum42
Range11.2
Interquartile range (IQR)0.9444444444

Descriptive statistics

Standard deviation0.7998972238
Coefficient of variation (CV)0.02137117995
Kurtosis2.713720453
Mean37.42878145
Median Absolute Deviation (MAD)0.4444444444
Skewness0.2732416694
Sum688240.4333
Variance0.6398355686
MonotonicityNot monotonic
2022-09-14T23:17:56.377914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37794
 
3.8%
37.11111111726
 
3.5%
37.16666667667
 
3.2%
37.05555556665
 
3.2%
36.88888889585
 
2.8%
37.22222222570
 
2.7%
36.66666667550
 
2.6%
37.27777778539
 
2.6%
36.94444444526
 
2.5%
37.33333333516
 
2.5%
Other values (193)12250
58.7%
(Missing)2497
 
12.0%
ValueCountFrequency (%)
30.81
< 0.1%
30.888888891
< 0.1%
31.61
< 0.1%
321
< 0.1%
32.21
< 0.1%
32.41
< 0.1%
32.51
< 0.1%
32.61
< 0.1%
32.611111111
< 0.1%
32.71
< 0.1%
ValueCountFrequency (%)
421
 
< 0.1%
41.61
 
< 0.1%
41.333333332
< 0.1%
41.111111111
 
< 0.1%
411
 
< 0.1%
40.944444441
 
< 0.1%
40.91
 
< 0.1%
40.833333331
 
< 0.1%
40.611111111
 
< 0.1%
40.63
< 0.1%

TempC_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4353
Distinct (%)23.7%
Missing2497
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean36.7517171
Minimum30.66666667
Maximum40.23833333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:56.575060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30.66666667
5-th percentile35.88888889
Q136.38888889
median36.73148148
Q337.09259259
95-th percentile37.75308642
Maximum40.23833333
Range9.571666667
Interquartile range (IQR)0.7037037037

Descriptive statistics

Standard deviation0.6034758027
Coefficient of variation (CV)0.01642034306
Kurtosis5.381340767
Mean36.7517171
Median Absolute Deviation (MAD)0.3481481481
Skewness-0.4430801678
Sum675790.574
Variance0.3641830444
MonotonicityNot monotonic
2022-09-14T23:17:56.792390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.66666667139
 
0.7%
36.72222222132
 
0.6%
36.77777778129
 
0.6%
36.55555556124
 
0.6%
36.61111111121
 
0.6%
36.5113
 
0.5%
36.83333333112
 
0.5%
36.88888889111
 
0.5%
36.3888888998
 
0.5%
3794
 
0.5%
Other values (4343)17215
82.4%
(Missing)2497
 
12.0%
ValueCountFrequency (%)
30.666666671
< 0.1%
30.888888891
< 0.1%
31.61
< 0.1%
31.694444441
< 0.1%
321
< 0.1%
32.015789471
< 0.1%
32.11
< 0.1%
32.21
< 0.1%
32.336111111
< 0.1%
32.351
< 0.1%
ValueCountFrequency (%)
40.238333331
< 0.1%
40.11
< 0.1%
40.055555561
< 0.1%
401
< 0.1%
39.724074071
< 0.1%
39.680888891
< 0.1%
39.666666671
< 0.1%
39.535353541
< 0.1%
39.462962961
< 0.1%
39.357142861
< 0.1%

SpO2_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct93
Distinct (%)0.5%
Missing2203
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean91.00749384
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:56.961828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile80
Q190
median92
Q394
95-th percentile97
Maximum100
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.39939013
Coefficient of variation (CV)0.08130528397
Kurtosis39.18688733
Mean91.00749384
Median Absolute Deviation (MAD)2
Skewness-5.003111398
Sum1700202
Variance54.7509743
MonotonicityNot monotonic
2022-09-14T23:17:57.125867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
932394
11.5%
922286
10.9%
942231
10.7%
951766
8.5%
911719
8.2%
901358
 
6.5%
961228
 
5.9%
89820
 
3.9%
97781
 
3.7%
88637
 
3.1%
Other values (83)3462
16.6%
(Missing)2203
10.5%
ValueCountFrequency (%)
13
< 0.1%
22
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
81
 
< 0.1%
94
< 0.1%
112
< 0.1%
133
< 0.1%
141
 
< 0.1%
ValueCountFrequency (%)
100127
 
0.6%
99236
 
1.1%
98448
 
2.1%
97781
 
3.7%
961228
5.9%
951766
8.5%
942231
10.7%
932394
11.5%
922286
10.9%
911719
8.2%

SpO2_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct22
Distinct (%)0.1%
Missing2203
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean99.55588267
Minimum57
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:57.271334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile97.05
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.159792434
Coefficient of variation (CV)0.01164966251
Kurtosis228.3208101
Mean99.55588267
Median Absolute Deviation (MAD)0
Skewness-9.698138761
Sum1859903
Variance1.345118489
MonotonicityNot monotonic
2022-09-14T23:17:57.393578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10014313
68.5%
992286
 
10.9%
981148
 
5.5%
97550
 
2.6%
96233
 
1.1%
9577
 
0.4%
9425
 
0.1%
9313
 
0.1%
9111
 
0.1%
9210
 
< 0.1%
Other values (12)16
 
0.1%
(Missing)2203
 
10.5%
ValueCountFrequency (%)
571
 
< 0.1%
671
 
< 0.1%
692
< 0.1%
701
 
< 0.1%
791
 
< 0.1%
801
 
< 0.1%
811
 
< 0.1%
821
 
< 0.1%
851
 
< 0.1%
883
< 0.1%
ValueCountFrequency (%)
10014313
68.5%
992286
 
10.9%
981148
 
5.5%
97550
 
2.6%
96233
 
1.1%
9577
 
0.4%
9425
 
0.1%
9313
 
0.1%
9210
 
< 0.1%
9111
 
0.1%

SpO2_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6366
Distinct (%)34.1%
Missing2203
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean96.86668507
Minimum47.66666667
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:57.533391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum47.66666667
5-th percentile93.6
Q195.8125
median97.14285714
Q398.34615385
95-th percentile99.58923319
Maximum100
Range52.33333333
Interquartile range (IQR)2.533653846

Descriptive statistics

Standard deviation2.333108256
Coefficient of variation (CV)0.02408576545
Kurtosis57.03712063
Mean96.86668507
Median Absolute Deviation (MAD)1.257142857
Skewness-4.552082192
Sum1809663.41
Variance5.443394136
MonotonicityNot monotonic
2022-09-14T23:17:57.690767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97143
 
0.7%
100127
 
0.6%
98114
 
0.5%
99111
 
0.5%
96107
 
0.5%
9570
 
0.3%
97.568
 
0.3%
98.561
 
0.3%
96.553
 
0.3%
97.6666666745
 
0.2%
Other values (6356)17783
85.1%
(Missing)2203
 
10.5%
ValueCountFrequency (%)
47.666666671
< 0.1%
55.692307691
< 0.1%
55.869565221
< 0.1%
56.555555561
< 0.1%
56.857142861
< 0.1%
571
< 0.1%
57.857142861
< 0.1%
651
< 0.1%
65.411764711
< 0.1%
671
< 0.1%
ValueCountFrequency (%)
100127
0.6%
99.98379631
 
< 0.1%
99.980291351
 
< 0.1%
99.979591841
 
< 0.1%
99.978260872
 
< 0.1%
99.977272732
 
< 0.1%
99.976744194
 
< 0.1%
99.976190482
 
< 0.1%
99.975609762
 
< 0.1%
99.974358971
 
< 0.1%

Glucose_Min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct326
Distinct (%)1.6%
Missing253
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean106.7819746
Minimum2
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:57.857831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile62
Q186
median102
Q3121
95-th percentile166
Maximum563
Range561
Interquartile range (IQR)35

Descriptive statistics

Standard deviation35.17881088
Coefficient of variation (CV)0.3294452178
Kurtosis11.56747541
Mean106.7819746
Median Absolute Deviation (MAD)17
Skewness2.045072888
Sum2203125.7
Variance1237.548735
MonotonicityNot monotonic
2022-09-14T23:17:58.042713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88376
 
1.8%
92366
 
1.8%
94366
 
1.8%
99351
 
1.7%
91348
 
1.7%
97346
 
1.7%
98342
 
1.6%
89334
 
1.6%
100333
 
1.6%
93332
 
1.6%
Other values (316)17138
82.1%
ValueCountFrequency (%)
21
 
< 0.1%
5.71
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
93
< 0.1%
101
 
< 0.1%
122
< 0.1%
142
< 0.1%
151
 
< 0.1%
163
< 0.1%
ValueCountFrequency (%)
5631
< 0.1%
4801
< 0.1%
4791
< 0.1%
4602
< 0.1%
4551
< 0.1%
4541
< 0.1%
4481
< 0.1%
4321
< 0.1%
4251
< 0.1%
4241
< 0.1%

Glucose_Max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct598
Distinct (%)2.9%
Missing253
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean182.1296045
Minimum42
Maximum2440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:58.238616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile95
Q1126
median161
Q3206
95-th percentile352
Maximum2440
Range2398
Interquartile range (IQR)80

Descriptive statistics

Standard deviation92.66560277
Coefficient of variation (CV)0.5087893483
Kurtosis47.10599705
Mean182.1296045
Median Absolute Deviation (MAD)38
Skewness4.058114258
Sum3757698
Variance8586.913937
MonotonicityNot monotonic
2022-09-14T23:17:58.413481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152177
 
0.8%
126167
 
0.8%
141165
 
0.8%
140164
 
0.8%
150164
 
0.8%
117162
 
0.8%
138162
 
0.8%
165162
 
0.8%
146161
 
0.8%
131159
 
0.8%
Other values (588)18989
90.9%
(Missing)253
 
1.2%
ValueCountFrequency (%)
421
 
< 0.1%
511
 
< 0.1%
524
< 0.1%
531
 
< 0.1%
541
 
< 0.1%
552
< 0.1%
561
 
< 0.1%
571
 
< 0.1%
582
< 0.1%
592
< 0.1%
ValueCountFrequency (%)
24401
< 0.1%
22861
< 0.1%
17461
< 0.1%
16381
< 0.1%
12291
< 0.1%
11151
< 0.1%
10521
< 0.1%
10451
< 0.1%
10381
< 0.1%
10161
< 0.1%

Glucose_Mean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6219
Distinct (%)30.1%
Missing253
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean138.8564278
Minimum42
Maximum771.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:17:58.764233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile88.85714286
Q1110.75
median128.6666667
Q3154.5785714
95-th percentile224.63
Maximum771.9
Range729.9
Interquartile range (IQR)43.82857143

Descriptive statistics

Standard deviation44.93314485
Coefficient of variation (CV)0.3235942733
Kurtosis10.53483136
Mean138.8564278
Median Absolute Deviation (MAD)20.66666667
Skewness2.207930714
Sum2864885.818
Variance2018.987506
MonotonicityNot monotonic
2022-09-14T23:17:58.913176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108115
 
0.6%
112114
 
0.5%
110108
 
0.5%
111107
 
0.5%
124102
 
0.5%
9999
 
0.5%
9499
 
0.5%
10697
 
0.5%
10295
 
0.5%
12394
 
0.5%
Other values (6209)19602
93.9%
(Missing)253
 
1.2%
ValueCountFrequency (%)
421
 
< 0.1%
43.251
 
< 0.1%
451
 
< 0.1%
511
 
< 0.1%
51.51
 
< 0.1%
523
< 0.1%
52.751
 
< 0.1%
541
 
< 0.1%
552
< 0.1%
55.51
 
< 0.1%
ValueCountFrequency (%)
771.91
< 0.1%
661.751
< 0.1%
611.61
< 0.1%
5801
< 0.1%
5791
< 0.1%
5631
< 0.1%
557.41
< 0.1%
551.1251
< 0.1%
534.33333331
< 0.1%
533.66666671
< 0.1%

GENDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
M
11759 
F
9126 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20885
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M11759
56.3%
F9126
43.7%

Length

2022-09-14T23:17:59.065530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-14T23:17:59.189629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
m11759
56.3%
f9126
43.7%

Most occurring characters

ValueCountFrequency (%)
M11759
56.3%
F9126
43.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter20885
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M11759
56.3%
F9126
43.7%

Most occurring scripts

ValueCountFrequency (%)
Latin20885
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M11759
56.3%
F9126
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII20885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M11759
56.3%
F9126
43.7%

DOB
Categorical

HIGH CARDINALITY
UNIFORM

Distinct14007
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2117-08-07 00:00:00
 
26
2096-10-22 00:00:00
 
16
2112-11-14 00:00:00
 
16
2105-05-05 00:00:00
 
15
2144-09-28 00:00:00
 
15
Other values (14002)
20797 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters396815
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9815 ?
Unique (%)47.0%

Sample

1st row2108-07-16 00:00:00
2nd row2087-01-16 00:00:00
3rd row2057-09-17 00:00:00
4th row2056-02-27 00:00:00
5th row2066-12-19 00:00:00

Common Values

ValueCountFrequency (%)
2117-08-07 00:00:0026
 
0.1%
2096-10-22 00:00:0016
 
0.1%
2112-11-14 00:00:0016
 
0.1%
2105-05-05 00:00:0015
 
0.1%
2144-09-28 00:00:0015
 
0.1%
2089-12-13 00:00:0015
 
0.1%
2106-01-28 00:00:0012
 
0.1%
2120-10-31 00:00:0012
 
0.1%
2045-04-02 00:00:0012
 
0.1%
2071-06-27 00:00:0011
 
0.1%
Other values (13997)20735
99.3%

Length

2022-09-14T23:17:59.307455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:0020885
50.0%
2117-08-0726
 
0.1%
2096-10-2216
 
< 0.1%
2112-11-1416
 
< 0.1%
2105-05-0515
 
< 0.1%
2144-09-2815
 
< 0.1%
2089-12-1315
 
< 0.1%
2106-01-2812
 
< 0.1%
2120-10-3112
 
< 0.1%
2045-04-0212
 
< 0.1%
Other values (13998)20746
49.7%

Most occurring characters

ValueCountFrequency (%)
0167412
42.2%
-41770
 
10.5%
:41770
 
10.5%
236040
 
9.1%
131077
 
7.8%
20885
 
5.3%
89210
 
2.3%
39045
 
2.3%
58173
 
2.1%
97895
 
2.0%
Other values (3)23538
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number292390
73.7%
Dash Punctuation41770
 
10.5%
Other Punctuation41770
 
10.5%
Space Separator20885
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0167412
57.3%
236040
 
12.3%
131077
 
10.6%
89210
 
3.1%
39045
 
3.1%
58173
 
2.8%
97895
 
2.7%
67884
 
2.7%
77870
 
2.7%
47784
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
-41770
100.0%
Other Punctuation
ValueCountFrequency (%)
:41770
100.0%
Space Separator
ValueCountFrequency (%)
20885
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common396815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0167412
42.2%
-41770
 
10.5%
:41770
 
10.5%
236040
 
9.1%
131077
 
7.8%
20885
 
5.3%
89210
 
2.3%
39045
 
2.3%
58173
 
2.1%
97895
 
2.0%
Other values (3)23538
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII396815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0167412
42.2%
-41770
 
10.5%
:41770
 
10.5%
236040
 
9.1%
131077
 
7.8%
20885
 
5.3%
89210
 
2.3%
39045
 
2.3%
58173
 
2.1%
97895
 
2.0%
Other values (3)23538
 
5.9%

DOD
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct4901
Distinct (%)66.5%
Missing13511
Missing (%)64.7%
Memory size969.6 KiB
2142-08-30 00:00:00
 
25
2156-11-06 00:00:00
 
14
2148-10-23 00:00:00
 
12
2111-07-01 00:00:00
 
12
2182-11-26 00:00:00
 
10
Other values (4896)
7301 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters140106
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3474 ?
Unique (%)47.1%

Sample

1st row2180-03-09 00:00:00
2nd row2132-03-01 00:00:00
3rd row2147-01-18 00:00:00
4th row2118-03-09 00:00:00
5th row2107-07-28 00:00:00

Common Values

ValueCountFrequency (%)
2142-08-30 00:00:0025
 
0.1%
2156-11-06 00:00:0014
 
0.1%
2148-10-23 00:00:0012
 
0.1%
2111-07-01 00:00:0012
 
0.1%
2182-11-26 00:00:0010
 
< 0.1%
2195-04-10 00:00:0010
 
< 0.1%
2173-12-24 00:00:0010
 
< 0.1%
2172-03-03 00:00:0010
 
< 0.1%
2172-01-26 00:00:009
 
< 0.1%
2162-11-14 00:00:009
 
< 0.1%
Other values (4891)7253
34.7%
(Missing)13511
64.7%

Length

2022-09-14T23:17:59.427519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:007374
50.0%
2142-08-3025
 
0.2%
2156-11-0614
 
0.1%
2148-10-2312
 
0.1%
2111-07-0112
 
0.1%
2182-11-2610
 
0.1%
2195-04-1010
 
0.1%
2173-12-2410
 
0.1%
2172-03-0310
 
0.1%
2172-01-269
 
0.1%
Other values (4892)7262
49.2%

Most occurring characters

ValueCountFrequency (%)
054820
39.1%
114926
 
10.7%
-14748
 
10.5%
:14748
 
10.5%
213481
 
9.6%
7374
 
5.3%
33190
 
2.3%
72896
 
2.1%
82866
 
2.0%
42801
 
2.0%
Other values (3)8256
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number103236
73.7%
Dash Punctuation14748
 
10.5%
Other Punctuation14748
 
10.5%
Space Separator7374
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
054820
53.1%
114926
 
14.5%
213481
 
13.1%
33190
 
3.1%
72896
 
2.8%
82866
 
2.8%
42801
 
2.7%
52788
 
2.7%
92749
 
2.7%
62719
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
-14748
100.0%
Other Punctuation
ValueCountFrequency (%)
:14748
100.0%
Space Separator
ValueCountFrequency (%)
7374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common140106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
054820
39.1%
114926
 
10.7%
-14748
 
10.5%
:14748
 
10.5%
213481
 
9.6%
7374
 
5.3%
33190
 
2.3%
72896
 
2.1%
82866
 
2.0%
42801
 
2.0%
Other values (3)8256
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII140106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
054820
39.1%
114926
 
10.7%
-14748
 
10.5%
:14748
 
10.5%
213481
 
9.6%
7374
 
5.3%
33190
 
2.3%
72896
 
2.1%
82866
 
2.0%
42801
 
2.0%
Other values (3)8256
 
5.9%

ADMITTIME
Categorical

HIGH CARDINALITY
UNIFORM

Distinct19714
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2165-02-25 11:40:00
 
5
2150-11-03 22:05:00
 
5
2169-10-31 20:11:00
 
5
2177-04-13 17:36:00
 
4
2176-02-27 17:39:00
 
4
Other values (19709)
20862 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters396815
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18668 ?
Unique (%)89.4%

Sample

1st row2178-02-06 10:35:00
2nd row2129-02-12 22:34:00
3rd row2125-11-17 23:04:00
4th row2131-01-26 08:00:00
5th row2146-05-04 02:02:00

Common Values

ValueCountFrequency (%)
2165-02-25 11:40:005
 
< 0.1%
2150-11-03 22:05:005
 
< 0.1%
2169-10-31 20:11:005
 
< 0.1%
2177-04-13 17:36:004
 
< 0.1%
2176-02-27 17:39:004
 
< 0.1%
2112-08-17 16:16:004
 
< 0.1%
2174-04-22 07:15:004
 
< 0.1%
2171-01-30 00:52:004
 
< 0.1%
2180-07-17 01:51:004
 
< 0.1%
2119-01-17 21:27:004
 
< 0.1%
Other values (19704)20842
99.8%

Length

2022-09-14T23:17:59.546422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07:15:001242
 
3.0%
08:00:00335
 
0.8%
14:00:00178
 
0.4%
12:00:00146
 
0.3%
11:30:0097
 
0.2%
10:30:0079
 
0.2%
07:30:0075
 
0.2%
12:30:0067
 
0.2%
13:00:0066
 
0.2%
11:00:0065
 
0.2%
Other values (16750)39420
94.4%

Most occurring characters

ValueCountFrequency (%)
087523
22.1%
160955
15.4%
249528
12.5%
-41770
10.5%
:41770
10.5%
20885
 
5.3%
316802
 
4.2%
515640
 
3.9%
414625
 
3.7%
712563
 
3.2%
Other values (3)34754
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number292390
73.7%
Dash Punctuation41770
 
10.5%
Other Punctuation41770
 
10.5%
Space Separator20885
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
087523
29.9%
160955
20.8%
249528
16.9%
316802
 
5.7%
515640
 
5.3%
414625
 
5.0%
712563
 
4.3%
811893
 
4.1%
611723
 
4.0%
911138
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
-41770
100.0%
Other Punctuation
ValueCountFrequency (%)
:41770
100.0%
Space Separator
ValueCountFrequency (%)
20885
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common396815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
087523
22.1%
160955
15.4%
249528
12.5%
-41770
10.5%
:41770
10.5%
20885
 
5.3%
316802
 
4.2%
515640
 
3.9%
414625
 
3.7%
712563
 
3.2%
Other values (3)34754
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII396815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
087523
22.1%
160955
15.4%
249528
12.5%
-41770
10.5%
:41770
10.5%
20885
 
5.3%
316802
 
4.2%
515640
 
3.9%
414625
 
3.7%
712563
 
3.2%
Other values (3)34754
 
8.8%

DISCHTIME
Categorical

HIGH CARDINALITY
UNIFORM

Distinct19706
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2165-07-06 20:27:00
 
5
2170-01-12 15:20:00
 
5
2151-02-17 12:00:00
 
5
2179-11-24 13:15:00
 
4
2112-08-11 14:13:00
 
4
Other values (19701)
20862 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters396815
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18652 ?
Unique (%)89.3%

Sample

1st row2178-02-13 18:30:00
2nd row2129-02-13 16:20:00
3rd row2125-12-05 17:55:00
4th row2131-02-05 16:23:00
5th row2146-05-20 18:40:00

Common Values

ValueCountFrequency (%)
2165-07-06 20:27:005
 
< 0.1%
2170-01-12 15:20:005
 
< 0.1%
2151-02-17 12:00:005
 
< 0.1%
2179-11-24 13:15:004
 
< 0.1%
2112-08-11 14:13:004
 
< 0.1%
2156-12-13 19:28:004
 
< 0.1%
2129-02-22 17:44:004
 
< 0.1%
2174-08-23 10:19:004
 
< 0.1%
2101-07-01 20:00:004
 
< 0.1%
2176-05-15 18:45:004
 
< 0.1%
Other values (19696)20842
99.8%

Length

2022-09-14T23:17:59.666840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
16:00:00485
 
1.2%
15:00:00449
 
1.1%
14:00:00429
 
1.0%
15:30:00407
 
1.0%
17:00:00397
 
1.0%
14:30:00389
 
0.9%
16:30:00375
 
0.9%
13:30:00316
 
0.8%
17:30:00301
 
0.7%
12:00:00297
 
0.7%
Other values (16230)37925
90.8%

Most occurring characters

ValueCountFrequency (%)
088068
22.2%
165960
16.6%
243878
11.1%
-41770
10.5%
:41770
10.5%
20885
 
5.3%
518160
 
4.6%
316933
 
4.3%
415571
 
3.9%
611896
 
3.0%
Other values (3)31924
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number292390
73.7%
Dash Punctuation41770
 
10.5%
Other Punctuation41770
 
10.5%
Space Separator20885
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
088068
30.1%
165960
22.6%
243878
15.0%
518160
 
6.2%
316933
 
5.8%
415571
 
5.3%
611896
 
4.1%
711400
 
3.9%
810920
 
3.7%
99604
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
-41770
100.0%
Other Punctuation
ValueCountFrequency (%)
:41770
100.0%
Space Separator
ValueCountFrequency (%)
20885
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common396815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
088068
22.2%
165960
16.6%
243878
11.1%
-41770
10.5%
:41770
10.5%
20885
 
5.3%
518160
 
4.6%
316933
 
4.3%
415571
 
3.9%
611896
 
3.0%
Other values (3)31924
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII396815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
088068
22.2%
165960
16.6%
243878
11.1%
-41770
10.5%
:41770
10.5%
20885
 
5.3%
518160
 
4.6%
316933
 
4.3%
415571
 
3.9%
611896
 
3.0%
Other values (3)31924
 
8.0%

DEATHTIME
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct2093
Distinct (%)89.3%
Missing18540
Missing (%)88.8%
Memory size753.5 KiB
2151-02-17 12:00:00
 
5
2165-07-06 20:27:00
 
5
2112-10-30 12:38:00
 
4
2174-08-23 10:19:00
 
4
2177-04-30 12:00:00
 
4
Other values (2088)
2323 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters44555
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1883 ?
Unique (%)80.3%

Sample

1st row2129-10-15 13:30:00
2nd row2172-03-08 13:20:00
3rd row2126-11-09 03:59:00
4th row2140-06-13 06:15:00
5th row2163-11-17 21:30:00

Common Values

ValueCountFrequency (%)
2151-02-17 12:00:005
 
< 0.1%
2165-07-06 20:27:005
 
< 0.1%
2112-10-30 12:38:004
 
< 0.1%
2174-08-23 10:19:004
 
< 0.1%
2177-04-30 12:00:004
 
< 0.1%
2176-05-15 18:45:004
 
< 0.1%
2129-02-22 17:44:004
 
< 0.1%
2156-12-13 19:28:004
 
< 0.1%
2167-12-15 01:45:003
 
< 0.1%
2132-07-05 01:00:003
 
< 0.1%
Other values (2083)2305
 
11.0%
(Missing)18540
88.8%

Length

2022-09-14T23:17:59.782141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00151
 
3.2%
00:00:0047
 
1.0%
00:01:0027
 
0.6%
15:00:0020
 
0.4%
04:00:0017
 
0.4%
08:00:0016
 
0.3%
01:00:0015
 
0.3%
04:30:0015
 
0.3%
17:30:0015
 
0.3%
15:30:0015
 
0.3%
Other values (2780)4352
92.8%

Most occurring characters

ValueCountFrequency (%)
011048
24.8%
16543
14.7%
25338
12.0%
-4690
10.5%
:4690
10.5%
2345
 
5.3%
51993
 
4.5%
31727
 
3.9%
41557
 
3.5%
81182
 
2.7%
Other values (3)3442
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32830
73.7%
Dash Punctuation4690
 
10.5%
Other Punctuation4690
 
10.5%
Space Separator2345
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011048
33.7%
16543
19.9%
25338
16.3%
51993
 
6.1%
31727
 
5.3%
41557
 
4.7%
81182
 
3.6%
71165
 
3.5%
91157
 
3.5%
61120
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
-4690
100.0%
Other Punctuation
ValueCountFrequency (%)
:4690
100.0%
Space Separator
ValueCountFrequency (%)
2345
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common44555
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011048
24.8%
16543
14.7%
25338
12.0%
-4690
10.5%
:4690
10.5%
2345
 
5.3%
51993
 
4.5%
31727
 
3.9%
41557
 
3.5%
81182
 
2.7%
Other values (3)3442
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII44555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011048
24.8%
16543
14.7%
25338
12.0%
-4690
10.5%
:4690
10.5%
2345
 
5.3%
51993
 
4.5%
31727
 
3.9%
41557
 
3.5%
81182
 
2.7%
Other values (3)3442
 
7.7%

Diff
Real number (ℝ)

Distinct16317
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-51617.06983
Minimum-72740.27444
Maximum-32157.49458
Zeros0
Zeros (%)0.0%
Negative20885
Negative (%)100.0%
Memory size163.3 KiB
2022-09-14T23:17:59.911553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-72740.27444
5-th percentile-68276.09583
Q1-60864.45411
median-51561.70346
Q3-42327.56003
95-th percentile-34962.70801
Maximum-32157.49458
Range40582.77986
Interquartile range (IQR)18536.89408

Descriptive statistics

Standard deviation10686.39585
Coefficient of variation (CV)-0.2070322062
Kurtosis-1.184788381
Mean-51617.06983
Median Absolute Deviation (MAD)9263.88625
Skewness-0.007334920605
Sum-1078022503
Variance114199056.2
MonotonicityNot monotonic
2022-09-14T23:18:00.078602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-48600.9416825
 
0.1%
-49364.9232616
 
0.1%
-50424.6061916
 
0.1%
-63198.2863215
 
0.1%
-52787.6061114
 
0.1%
-44199.4708513
 
0.1%
-49736.7843112
 
0.1%
-36049.4166812
 
0.1%
-45615.2548211
 
0.1%
-43280.5557511
 
0.1%
Other values (16307)20740
99.3%
ValueCountFrequency (%)
-72740.274441
 
< 0.1%
-72059.605851
 
< 0.1%
-72049.099961
 
< 0.1%
-72042.607131
 
< 0.1%
-71904.386031
 
< 0.1%
-71865.68671
 
< 0.1%
-71863.275475
< 0.1%
-71828.392532
 
< 0.1%
-71823.755691
 
< 0.1%
-71762.420282
 
< 0.1%
ValueCountFrequency (%)
-32157.494581
 
< 0.1%
-32176.060991
 
< 0.1%
-32181.92982
< 0.1%
-32226.634441
 
< 0.1%
-32274.883043
< 0.1%
-32278.004391
 
< 0.1%
-32286.823071
 
< 0.1%
-32302.557081
 
< 0.1%
-32335.123041
 
< 0.1%
-32354.390171
 
< 0.1%

ADMISSION_TYPE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
EMERGENCY
17817 
ELECTIVE
2848 
URGENT
 
220

Length

Max length9
Median length9
Mean length8.832032559
Min length6

Characters and Unicode

Total characters184457
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMERGENCY
2nd rowEMERGENCY
3rd rowEMERGENCY
4th rowELECTIVE
5th rowEMERGENCY

Common Values

ValueCountFrequency (%)
EMERGENCY17817
85.3%
ELECTIVE2848
 
13.6%
URGENT220
 
1.1%

Length

2022-09-14T23:18:00.232924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-14T23:18:00.373086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
emergency17817
85.3%
elective2848
 
13.6%
urgent220
 
1.1%

Most occurring characters

ValueCountFrequency (%)
E62215
33.7%
C20665
 
11.2%
R18037
 
9.8%
G18037
 
9.8%
N18037
 
9.8%
M17817
 
9.7%
Y17817
 
9.7%
T3068
 
1.7%
L2848
 
1.5%
I2848
 
1.5%
Other values (2)3068
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter184457
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E62215
33.7%
C20665
 
11.2%
R18037
 
9.8%
G18037
 
9.8%
N18037
 
9.8%
M17817
 
9.7%
Y17817
 
9.7%
T3068
 
1.7%
L2848
 
1.5%
I2848
 
1.5%
Other values (2)3068
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin184457
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E62215
33.7%
C20665
 
11.2%
R18037
 
9.8%
G18037
 
9.8%
N18037
 
9.8%
M17817
 
9.7%
Y17817
 
9.7%
T3068
 
1.7%
L2848
 
1.5%
I2848
 
1.5%
Other values (2)3068
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII184457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E62215
33.7%
C20665
 
11.2%
R18037
 
9.8%
G18037
 
9.8%
N18037
 
9.8%
M17817
 
9.7%
Y17817
 
9.7%
T3068
 
1.7%
L2848
 
1.5%
I2848
 
1.5%
Other values (2)3068
 
1.7%

INSURANCE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Medicare
11718 
Private
6245 
Medicaid
2117 
Government
 
611
Self Pay
 
194

Length

Max length10
Median length8
Mean length7.759492459
Min length7

Characters and Unicode

Total characters162057
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedicare
2nd rowPrivate
3rd rowMedicare
4th rowMedicare
5th rowMedicare

Common Values

ValueCountFrequency (%)
Medicare11718
56.1%
Private6245
29.9%
Medicaid2117
 
10.1%
Government611
 
2.9%
Self Pay194
 
0.9%

Length

2022-09-14T23:18:00.499254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-14T23:18:00.666794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
medicare11718
55.6%
private6245
29.6%
medicaid2117
 
10.0%
government611
 
2.9%
self194
 
0.9%
pay194
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e33214
20.5%
i22197
13.7%
a20274
12.5%
r18574
11.5%
d15952
9.8%
M13835
8.5%
c13835
8.5%
v6856
 
4.2%
t6856
 
4.2%
P6439
 
4.0%
Other values (9)4025
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter140784
86.9%
Uppercase Letter21079
 
13.0%
Space Separator194
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e33214
23.6%
i22197
15.8%
a20274
14.4%
r18574
13.2%
d15952
11.3%
c13835
9.8%
v6856
 
4.9%
t6856
 
4.9%
n1222
 
0.9%
o611
 
0.4%
Other values (4)1193
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
M13835
65.6%
P6439
30.5%
G611
 
2.9%
S194
 
0.9%
Space Separator
ValueCountFrequency (%)
194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin161863
99.9%
Common194
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e33214
20.5%
i22197
13.7%
a20274
12.5%
r18574
11.5%
d15952
9.9%
M13835
8.5%
c13835
8.5%
v6856
 
4.2%
t6856
 
4.2%
P6439
 
4.0%
Other values (8)3831
 
2.4%
Common
ValueCountFrequency (%)
194
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII162057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e33214
20.5%
i22197
13.7%
a20274
12.5%
r18574
11.5%
d15952
9.8%
M13835
8.5%
c13835
8.5%
v6856
 
4.2%
t6856
 
4.2%
P6439
 
4.0%
Other values (9)4025
 
2.5%

RELIGION
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
CATHOLIC
7655 
NOT SPECIFIED
5398 
PROTESTANT QUAKER
2753 
JEWISH
1840 
UNOBTAINABLE
1515 
Other values (12)
1724 

Length

Max length22
Median length19
Mean length10.7619344
Min length5

Characters and Unicode

Total characters224763
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPROTESTANT QUAKER
2nd rowUNOBTAINABLE
3rd rowPROTESTANT QUAKER
4th rowNOT SPECIFIED
5th rowJEWISH

Common Values

ValueCountFrequency (%)
CATHOLIC7655
36.7%
NOT SPECIFIED5398
25.8%
PROTESTANT QUAKER2753
 
13.2%
JEWISH1840
 
8.8%
UNOBTAINABLE1515
 
7.3%
OTHER702
 
3.4%
EPISCOPALIAN288
 
1.4%
GREEK ORTHODOX178
 
0.9%
CHRISTIAN SCIENTIST164
 
0.8%
BUDDHIST109
 
0.5%
Other values (7)283
 
1.4%

Length

2022-09-14T23:18:00.816456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
catholic7655
25.9%
not5398
18.3%
specified5398
18.3%
protestant2753
 
9.3%
quaker2753
 
9.3%
jewish1840
 
6.2%
unobtainable1515
 
5.1%
other702
 
2.4%
episcopalian288
 
1.0%
greek178
 
0.6%
Other values (16)1085
 
3.7%

Most occurring characters

ValueCountFrequency (%)
T24633
11.0%
I23591
10.5%
E21384
 
9.5%
C21324
 
9.5%
O18972
 
8.4%
A17321
 
7.7%
N12154
 
5.4%
S11268
 
5.0%
H10848
 
4.8%
L9586
 
4.3%
Other values (20)53682
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter215913
96.1%
Space Separator8680
 
3.9%
Other Punctuation86
 
< 0.1%
Dash Punctuation54
 
< 0.1%
Decimal Number30
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T24633
11.4%
I23591
10.9%
E21384
9.9%
C21324
9.9%
O18972
8.8%
A17321
 
8.0%
N12154
 
5.6%
S11268
 
5.2%
H10848
 
5.0%
L9586
 
4.4%
Other values (15)44832
20.8%
Other Punctuation
ValueCountFrequency (%)
'45
52.3%
.41
47.7%
Space Separator
ValueCountFrequency (%)
8680
100.0%
Dash Punctuation
ValueCountFrequency (%)
-54
100.0%
Decimal Number
ValueCountFrequency (%)
730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin215913
96.1%
Common8850
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T24633
11.4%
I23591
10.9%
E21384
9.9%
C21324
9.9%
O18972
8.8%
A17321
 
8.0%
N12154
 
5.6%
S11268
 
5.2%
H10848
 
5.0%
L9586
 
4.4%
Other values (15)44832
20.8%
Common
ValueCountFrequency (%)
8680
98.1%
-54
 
0.6%
'45
 
0.5%
.41
 
0.5%
730
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII224763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T24633
11.0%
I23591
10.5%
E21384
 
9.5%
C21324
 
9.5%
O18972
 
8.4%
A17321
 
7.7%
N12154
 
5.4%
S11268
 
5.0%
H10848
 
4.8%
L9586
 
4.3%
Other values (20)53682
23.9%

MARITAL_STATUS
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing722
Missing (%)3.5%
Memory size1.3 MiB
MARRIED
9664 
SINGLE
5910 
WIDOWED
2819 
DIVORCED
1413 
SEPARATED
 
240
Other values (2)
 
117

Length

Max length17
Median length7
Mean length6.855329068
Min length6

Characters and Unicode

Total characters138224
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSINGLE
2nd rowMARRIED
3rd rowSEPARATED
4th rowWIDOWED
5th rowWIDOWED

Common Values

ValueCountFrequency (%)
MARRIED9664
46.3%
SINGLE5910
28.3%
WIDOWED2819
 
13.5%
DIVORCED1413
 
6.8%
SEPARATED240
 
1.1%
UNKNOWN (DEFAULT)103
 
0.5%
LIFE PARTNER14
 
0.1%
(Missing)722
 
3.5%

Length

2022-09-14T23:18:00.959475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-14T23:18:01.118864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
married9664
47.7%
single5910
29.1%
widowed2819
 
13.9%
divorced1413
 
7.0%
separated240
 
1.2%
unknown103
 
0.5%
default103
 
0.5%
life14
 
0.1%
partner14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R21009
15.2%
E20417
14.8%
I19820
14.3%
D18471
13.4%
A10261
7.4%
M9664
7.0%
N6233
 
4.5%
S6150
 
4.4%
L6027
 
4.4%
G5910
 
4.3%
Other values (12)14262
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter137901
99.8%
Space Separator117
 
0.1%
Open Punctuation103
 
0.1%
Close Punctuation103
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R21009
15.2%
E20417
14.8%
I19820
14.4%
D18471
13.4%
A10261
7.4%
M9664
7.0%
N6233
 
4.5%
S6150
 
4.5%
L6027
 
4.4%
G5910
 
4.3%
Other values (9)13939
10.1%
Space Separator
ValueCountFrequency (%)
117
100.0%
Open Punctuation
ValueCountFrequency (%)
(103
100.0%
Close Punctuation
ValueCountFrequency (%)
)103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin137901
99.8%
Common323
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
R21009
15.2%
E20417
14.8%
I19820
14.4%
D18471
13.4%
A10261
7.4%
M9664
7.0%
N6233
 
4.5%
S6150
 
4.5%
L6027
 
4.4%
G5910
 
4.3%
Other values (9)13939
10.1%
Common
ValueCountFrequency (%)
117
36.2%
(103
31.9%
)103
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII138224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R21009
15.2%
E20417
14.8%
I19820
14.3%
D18471
13.4%
A10261
7.4%
M9664
7.0%
N6233
 
4.5%
S6150
 
4.4%
L6027
 
4.4%
G5910
 
4.3%
Other values (12)14262
10.3%

ETHNICITY
Categorical

HIGH CORRELATION

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
WHITE
15112 
BLACK/AFRICAN AMERICAN
1977 
UNABLE TO OBTAIN
 
577
UNKNOWN/NOT SPECIFIED
 
568
HISPANIC OR LATINO
 
562
Other values (36)
2089 

Length

Max length56
Median length5
Mean length8.698060809
Min length5

Characters and Unicode

Total characters181659
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWHITE
2nd rowWHITE
3rd rowBLACK/AFRICAN AMERICAN
4th rowWHITE
5th rowWHITE

Common Values

ValueCountFrequency (%)
WHITE15112
72.4%
BLACK/AFRICAN AMERICAN1977
 
9.5%
UNABLE TO OBTAIN577
 
2.8%
UNKNOWN/NOT SPECIFIED568
 
2.7%
HISPANIC OR LATINO562
 
2.7%
OTHER489
 
2.3%
ASIAN265
 
1.3%
PATIENT DECLINED TO ANSWER175
 
0.8%
HISPANIC/LATINO - PUERTO RICAN155
 
0.7%
ASIAN - CHINESE146
 
0.7%
Other values (31)859
 
4.1%

Length

2022-09-14T23:18:01.279020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white15330
53.9%
american2006
 
7.1%
black/african2003
 
7.0%
788
 
2.8%
to752
 
2.6%
asian603
 
2.1%
unable577
 
2.0%
obtain577
 
2.0%
unknown/not568
 
2.0%
specified568
 
2.0%
Other values (49)4661
 
16.4%

Most occurring characters

ValueCountFrequency (%)
I25800
14.2%
E21737
12.0%
T19535
10.8%
H17023
9.4%
W16079
8.9%
A16002
8.8%
N11535
 
6.3%
C8462
 
4.7%
7548
 
4.2%
R6139
 
3.4%
Other values (20)31799
17.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter170235
93.7%
Space Separator7548
 
4.2%
Other Punctuation3074
 
1.7%
Dash Punctuation788
 
0.4%
Open Punctuation7
 
< 0.1%
Close Punctuation7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I25800
15.2%
E21737
12.8%
T19535
11.5%
H17023
10.0%
W16079
9.4%
A16002
9.4%
N11535
6.8%
C8462
 
5.0%
R6139
 
3.6%
O4832
 
2.8%
Other values (15)23091
13.6%
Space Separator
ValueCountFrequency (%)
7548
100.0%
Other Punctuation
ValueCountFrequency (%)
/3074
100.0%
Dash Punctuation
ValueCountFrequency (%)
-788
100.0%
Open Punctuation
ValueCountFrequency (%)
(7
100.0%
Close Punctuation
ValueCountFrequency (%)
)7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin170235
93.7%
Common11424
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I25800
15.2%
E21737
12.8%
T19535
11.5%
H17023
10.0%
W16079
9.4%
A16002
9.4%
N11535
6.8%
C8462
 
5.0%
R6139
 
3.6%
O4832
 
2.8%
Other values (15)23091
13.6%
Common
ValueCountFrequency (%)
7548
66.1%
/3074
26.9%
-788
 
6.9%
(7
 
0.1%
)7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII181659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I25800
14.2%
E21737
12.0%
T19535
10.8%
H17023
9.4%
W16079
8.9%
A16002
8.8%
N11535
 
6.3%
C8462
 
4.7%
7548
 
4.2%
R6139
 
3.4%
Other values (20)31799
17.5%

DIAGNOSIS
Categorical

HIGH CARDINALITY

Distinct6193
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
PNEUMONIA
 
876
SEPSIS
 
481
ALTERED MENTAL STATUS
 
453
CONGESTIVE HEART FAILURE
 
425
INTRACRANIAL HEMORRHAGE
 
407
Other values (6188)
18243 

Length

Max length191
Median length141
Mean length22.97309073
Min length2

Characters and Unicode

Total characters479793
Distinct characters75
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4810 ?
Unique (%)23.0%

Sample

1st rowGASTROINTESTINAL BLEED
2nd rowESOPHAGEAL FOOD IMPACTION
3rd rowUPPER GI BLEED
4th rowHIATAL HERNIA/SDA
5th rowABDOMINAL PAIN

Common Values

ValueCountFrequency (%)
PNEUMONIA876
 
4.2%
SEPSIS481
 
2.3%
ALTERED MENTAL STATUS453
 
2.2%
CONGESTIVE HEART FAILURE425
 
2.0%
INTRACRANIAL HEMORRHAGE407
 
1.9%
UPPER GI BLEED370
 
1.8%
CHEST PAIN355
 
1.7%
CORONARY ARTERY DISEASE\CORONARY ARTERY BYPASS GRAFT /SDA355
 
1.7%
CORONARY ARTERY DISEASE305
 
1.5%
ABDOMINAL PAIN294
 
1.4%
Other values (6183)16564
79.3%

Length

2022-09-14T23:18:01.450357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
artery1904
 
3.4%
coronary1342
 
2.4%
bleed1321
 
2.4%
failure1054
 
1.9%
sda934
 
1.7%
pneumonia926
 
1.7%
heart910
 
1.6%
pain811
 
1.5%
aortic800
 
1.4%
hemorrhage788
 
1.4%
Other values (5031)44861
80.6%

Most occurring characters

ValueCountFrequency (%)
A49945
 
10.4%
E48409
 
10.1%
R38921
 
8.1%
34915
 
7.3%
I34348
 
7.2%
T33768
 
7.0%
S29557
 
6.2%
O26510
 
5.5%
N26352
 
5.5%
C20889
 
4.4%
Other values (65)136179
28.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter432305
90.1%
Space Separator34915
 
7.3%
Other Punctuation10998
 
2.3%
Lowercase Letter882
 
0.2%
Dash Punctuation337
 
0.1%
Decimal Number191
 
< 0.1%
Open Punctuation80
 
< 0.1%
Close Punctuation78
 
< 0.1%
Math Symbol7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A49945
11.6%
E48409
11.2%
R38921
 
9.0%
I34348
 
7.9%
T33768
 
7.8%
S29557
 
6.8%
O26510
 
6.1%
N26352
 
6.1%
C20889
 
4.8%
L20269
 
4.7%
Other values (16)103337
23.9%
Lowercase Letter
ValueCountFrequency (%)
a112
12.7%
t107
12.1%
e100
11.3%
r79
 
9.0%
l56
 
6.3%
n55
 
6.2%
i44
 
5.0%
c39
 
4.4%
o39
 
4.4%
p37
 
4.2%
Other values (12)214
24.3%
Other Punctuation
ValueCountFrequency (%)
;4061
36.9%
/3566
32.4%
\2194
19.9%
?364
 
3.3%
*340
 
3.1%
,337
 
3.1%
.49
 
0.4%
&41
 
0.4%
'38
 
0.3%
:7
 
0.1%
Decimal Number
ValueCountFrequency (%)
248
25.1%
136
18.8%
329
15.2%
523
12.0%
415
 
7.9%
012
 
6.3%
611
 
5.8%
710
 
5.2%
94
 
2.1%
83
 
1.6%
Open Punctuation
ValueCountFrequency (%)
(79
98.8%
[1
 
1.2%
Space Separator
ValueCountFrequency (%)
34915
100.0%
Dash Punctuation
ValueCountFrequency (%)
-337
100.0%
Close Punctuation
ValueCountFrequency (%)
)78
100.0%
Math Symbol
ValueCountFrequency (%)
+7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin433187
90.3%
Common46606
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A49945
11.5%
E48409
11.2%
R38921
 
9.0%
I34348
 
7.9%
T33768
 
7.8%
S29557
 
6.8%
O26510
 
6.1%
N26352
 
6.1%
C20889
 
4.8%
L20269
 
4.7%
Other values (38)104219
24.1%
Common
ValueCountFrequency (%)
34915
74.9%
;4061
 
8.7%
/3566
 
7.7%
\2194
 
4.7%
?364
 
0.8%
*340
 
0.7%
-337
 
0.7%
,337
 
0.7%
(79
 
0.2%
)78
 
0.2%
Other values (17)335
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII479793
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A49945
 
10.4%
E48409
 
10.1%
R38921
 
8.1%
34915
 
7.3%
I34348
 
7.2%
T33768
 
7.0%
S29557
 
6.2%
O26510
 
5.5%
N26352
 
5.5%
C20889
 
4.4%
Other values (65)136179
28.4%

ICD9_diagnosis
Categorical

HIGH CARDINALITY

Distinct1853
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
41401
 
1098
0389
 
986
41071
 
549
4241
 
534
431
 
444
Other values (1848)
17274 

Length

Max length5
Median length5
Mean length4.474503232
Min length3

Characters and Unicode

Total characters93450
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique694 ?
Unique (%)3.3%

Sample

1st row5789
2nd row53013
3rd row56983
4th row5533
5th row56211

Common Values

ValueCountFrequency (%)
414011098
 
5.3%
0389986
 
4.7%
41071549
 
2.6%
4241534
 
2.6%
431444
 
2.1%
51881399
 
1.9%
486319
 
1.5%
5070270
 
1.3%
430228
 
1.1%
42823223
 
1.1%
Other values (1843)15835
75.8%

Length

2022-09-14T23:18:01.612128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
414011098
 
5.3%
0389986
 
4.7%
41071549
 
2.6%
4241534
 
2.6%
431444
 
2.1%
51881399
 
1.9%
486319
 
1.5%
5070270
 
1.3%
430228
 
1.1%
42823223
 
1.1%
Other values (1843)15835
75.8%

Most occurring characters

ValueCountFrequency (%)
115398
16.5%
413462
14.4%
011702
12.5%
29756
10.4%
89642
10.3%
58201
8.8%
98175
8.7%
38126
8.7%
75143
 
5.5%
63780
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number93385
99.9%
Uppercase Letter65
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115398
16.5%
413462
14.4%
011702
12.5%
29756
10.4%
89642
10.3%
58201
8.8%
98175
8.8%
38126
8.7%
75143
 
5.5%
63780
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
V65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common93385
99.9%
Latin65
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
115398
16.5%
413462
14.4%
011702
12.5%
29756
10.4%
89642
10.3%
58201
8.8%
98175
8.8%
38126
8.7%
75143
 
5.5%
63780
 
4.0%
Latin
ValueCountFrequency (%)
V65
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII93450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115398
16.5%
413462
14.4%
011702
12.5%
29756
10.4%
89642
10.3%
58201
8.8%
98175
8.7%
38126
8.7%
75143
 
5.5%
63780
 
4.0%

FIRST_CAREUNIT
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
MICU
8640 
SICU
3961 
CSRU
3127 
TSICU
2645 
CCU
2512 

Length

Max length5
Median length4
Mean length4.006368207
Min length3

Characters and Unicode

Total characters83673
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMICU
2nd rowMICU
3rd rowMICU
4th rowSICU
5th rowTSICU

Common Values

ValueCountFrequency (%)
MICU8640
41.4%
SICU3961
19.0%
CSRU3127
 
15.0%
TSICU2645
 
12.7%
CCU2512
 
12.0%

Length

2022-09-14T23:18:01.746971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-14T23:18:01.897912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
micu8640
41.4%
sicu3961
19.0%
csru3127
 
15.0%
tsicu2645
 
12.7%
ccu2512
 
12.0%

Most occurring characters

ValueCountFrequency (%)
C23397
28.0%
U20885
25.0%
I15246
18.2%
S9733
11.6%
M8640
 
10.3%
R3127
 
3.7%
T2645
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter83673
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C23397
28.0%
U20885
25.0%
I15246
18.2%
S9733
11.6%
M8640
 
10.3%
R3127
 
3.7%
T2645
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin83673
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C23397
28.0%
U20885
25.0%
I15246
18.2%
S9733
11.6%
M8640
 
10.3%
R3127
 
3.7%
T2645
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII83673
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C23397
28.0%
U20885
25.0%
I15246
18.2%
S9733
11.6%
M8640
 
10.3%
R3127
 
3.7%
T2645
 
3.2%

LOS
Real number (ℝ≥0)

Distinct16891
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.701045559
Minimum0.0566
Maximum101.739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size163.3 KiB
2022-09-14T23:18:02.059803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0566
5-th percentile0.7479
Q11.1654
median2.0208
Q33.9158
95-th percentile12.78948
Maximum101.739
Range101.6824
Interquartile range (IQR)2.7504

Descriptive statistics

Standard deviation5.17572129
Coefficient of variation (CV)1.398448414
Kurtosis38.18368449
Mean3.701045559
Median Absolute Deviation (MAD)1.0058
Skewness4.864685757
Sum77296.3365
Variance26.78809087
MonotonicityNot monotonic
2022-09-14T23:18:02.218795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.08456
 
< 0.1%
0.84186
 
< 0.1%
1.12446
 
< 0.1%
1.02335
 
< 0.1%
1.15735
 
< 0.1%
1.71545
 
< 0.1%
0.88185
 
< 0.1%
1.125
 
< 0.1%
1.05095
 
< 0.1%
1.07785
 
< 0.1%
Other values (16881)20832
99.7%
ValueCountFrequency (%)
0.05661
< 0.1%
0.07051
< 0.1%
0.07971
< 0.1%
0.08921
< 0.1%
0.09621
< 0.1%
0.0991
< 0.1%
0.10241
< 0.1%
0.1051
< 0.1%
0.10761
< 0.1%
0.10911
< 0.1%
ValueCountFrequency (%)
101.7391
< 0.1%
86.84841
< 0.1%
79.1091
< 0.1%
67.97351
< 0.1%
66.37971
< 0.1%
63.5761
< 0.1%
63.45331
< 0.1%
61.92631
< 0.1%
61.91671
< 0.1%
61.4041
< 0.1%

Interactions

2022-09-14T23:17:39.994874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:09.399472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:15.928961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:22.427056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:30.069731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:35.872671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:41.202387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:48.510139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:54.209307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:59.451493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:04.637442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:09.483733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:14.225288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:20.253871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:25.819619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:31.937784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:36.576296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:41.581281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:46.056816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:50.834132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:55.282057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:00.303107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:05.515936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:10.313129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:14.718821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:19.235702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:23.656273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:29.761649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:34.550595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:40.159636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:09.612317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:16.310540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:22.799236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:30.304442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:36.018693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:41.548667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:48.676772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:54.385852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:59.614149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:04.799021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:09.683342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:14.373831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:20.404662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:25.974553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:32.101682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:36.723485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:41.732443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:46.196922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:50.971907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:55.413268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:00.482217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:05.685045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:10.453522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:14.863273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:19.381227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:23.992016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:29.916342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:34.749282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:40.360442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:09.819464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:16.517056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:22.970992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:30.487269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:36.185308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:42.063759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:48.833590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:54.584937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:59.782234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:04.950694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:09.914513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:14.519113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:20.574428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:26.194558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:32.283359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:36.872610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:41.878194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:46.339477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:51.113994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:55.549696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:00.643005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:05.825912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:10.597648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:15.013121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:19.523767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:24.144586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:30.090792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:34.939615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:40.510712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:10.087713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:16.832489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:23.187047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:30.684276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:36.364270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:42.435793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:49.145068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:54.763982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:15:59.948763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:05.118172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:10.097248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:14.672563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:20.733164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:26.417134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:32.451956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:37.220349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:42.033024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:46.480461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:51.258798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:16:55.685187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:00.787955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:06.003803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:10.741042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:15.173650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:19.678164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:24.291678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:30.247768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:35.158503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-14T23:17:40.654843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-09-14T23:17:39.848903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-14T23:18:02.590554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-14T23:18:02.952506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-14T23:18:03.330933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-14T23:18:03.653169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-14T23:18:03.885894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-14T23:17:44.775434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-14T23:17:46.316117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-14T23:17:47.348129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-14T23:17:48.065126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

HOSPITAL_EXPIRE_FLAGsubject_idhadm_idicustay_idHeartRate_MinHeartRate_MaxHeartRate_MeanSysBP_MinSysBP_MaxSysBP_MeanDiasBP_MinDiasBP_MaxDiasBP_MeanMeanBP_MinMeanBP_MaxMeanBP_MeanRespRate_MinRespRate_MaxRespRate_MeanTempC_MinTempC_MaxTempC_MeanSpO2_MinSpO2_MaxSpO2_MeanGlucose_MinGlucose_MaxGlucose_MeanGENDERDOBDODADMITTIMEDISCHTIMEDEATHTIMEDiffADMISSION_TYPEINSURANCERELIGIONMARITAL_STATUSETHNICITYDIAGNOSISICD9_diagnosisFIRST_CAREUNITLOS
005544019576822835789.0145.0121.04347874.0127.0106.58695742.090.061.17391359.094.074.54347815.030.022.34782635.11111136.94444436.08024790.099.095.739130111.0230.0160.777778F2108-07-16 00:00:002180-03-09 00:00:002178-02-06 10:35:002178-02-13 18:30:00NaN-61961.78470EMERGENCYMedicarePROTESTANT QUAKERSINGLEWHITEGASTROINTESTINAL BLEED5789MICU4.5761
107690812613622100463.0110.079.11764789.0121.0106.73333349.074.064.73333358.084.074.80000013.021.016.05882436.33333336.61111136.47222298.0100.099.058824103.0103.0103.000000F2087-01-16 00:00:00NaN2129-02-12 22:34:002129-02-13 16:20:00NaN-43146.18378EMERGENCYPrivateUNOBTAINABLEMARRIEDWHITEESOPHAGEAL FOOD IMPACTION53013MICU0.7582
209579813664529631581.098.091.68965588.0138.0112.78571445.067.056.82142964.088.072.88888913.021.015.90000036.44444436.88888936.666667100.0100.0100.000000132.0346.0217.636364F2057-09-17 00:00:00NaN2125-11-17 23:04:002125-12-05 17:55:00NaN-42009.96157EMERGENCYMedicarePROTESTANT QUAKERSEPARATEDBLACK/AFRICAN AMERICANUPPER GI BLEED56983MICU3.7626
304070810250524555776.0128.098.85714384.0135.0106.97297330.089.041.86486548.094.062.78378412.035.026.77142936.33333339.50000037.83333378.0100.095.085714108.0139.0125.000000F2056-02-27 00:00:002132-03-01 00:00:002131-01-26 08:00:002131-02-05 16:23:00NaN-43585.37922ELECTIVEMedicareNOT SPECIFIEDWIDOWEDWHITEHIATAL HERNIA/SDA5533SICU3.8734
4028424127337225281NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN97.0137.0113.000000F2066-12-19 00:00:002147-01-18 00:00:002146-05-04 02:02:002146-05-20 18:40:00NaN-50271.76602EMERGENCYMedicareJEWISHWIDOWEDWHITEABDOMINAL PAIN56211TSICU5.8654
506331118010228751970.0130.097.951220107.0155.0128.41463460.088.074.78048872.0100.086.82051310.023.015.70731736.00000036.94444436.59127096.0100.098.87804992.0142.0104.400000M2102-06-04 00:00:00NaN2151-08-06 19:11:002151-08-09 17:09:00NaN-51044.77754EMERGENCYPrivateCATHOLICSINGLEWHITELARGE GASTOINTESTINAL BLEED5550MICU1.8490
608237713485423116464.0116.087.448276104.0148.0122.10000049.072.056.40000069.092.078.79310312.024.015.90909135.22222238.33333336.80158794.0100.098.413793102.0204.0163.666667M2107-03-27 00:00:00NaN2176-01-18 02:05:002176-01-26 15:00:00NaN-59706.62840EMERGENCYMedicareOTHERMARRIEDWHITEABDOMINAL PAIN5513TSICU4.3669
708623318460623751462.0100.082.86206962.0154.0114.64285734.0113.056.96428648.0122.072.75000011.026.018.87878836.11111137.72222236.90740787.0100.096.931034116.0183.0142.166667F2103-11-21 00:00:00NaN2177-11-26 08:00:002177-12-10 15:45:00NaN-61550.06567ELECTIVEMedicarePROTESTANT QUAKERMARRIEDWHITELEFT LUNG CANCER/SDA1625SICU9.8213
805378717477224441384.0109.094.65217481.0163.0121.72727329.077.047.90909149.087.065.72727315.025.019.86956535.61111136.94444436.20370489.0100.092.913043233.0484.0361.000000F2089-09-14 00:00:00NaN2160-10-07 22:36:002160-10-11 15:45:00NaN-55760.96681EMERGENCYMedicareCATHOLICDIVORCEDWHITEASTHMA;COPD EXACERBATION49322MICU1.0230
909938416808729891974.098.081.14285784.0140.0113.87500035.072.054.34375031.081.066.80645217.028.023.26470635.88888937.11111136.65277888.099.094.60000085.0161.0112.000000M2032-05-26 00:00:002118-03-09 00:00:002117-12-15 18:12:002117-12-23 15:25:00NaN-39375.73369EMERGENCYMedicareBUDDHISTWIDOWEDWHITEPULMONARY EMBOLISM41511TSICU1.3265

Last rows

HOSPITAL_EXPIRE_FLAGsubject_idhadm_idicustay_idHeartRate_MinHeartRate_MaxHeartRate_MeanSysBP_MinSysBP_MaxSysBP_MeanDiasBP_MinDiasBP_MaxDiasBP_MeanMeanBP_MinMeanBP_MaxMeanBP_MeanRespRate_MinRespRate_MaxRespRate_MeanTempC_MinTempC_MaxTempC_MeanSpO2_MinSpO2_MaxSpO2_MeanGlucose_MinGlucose_MaxGlucose_MeanGENDERDOBDODADMITTIMEDISCHTIMEDEATHTIMEDiffADMISSION_TYPEINSURANCERELIGIONMARITAL_STATUSETHNICITYDIAGNOSISICD9_diagnosisFIRST_CAREUNITLOS
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